code: def __init__(
      self, num_classes, pretrained=True, include_top=False, freeze=True
  ):
      super().__init__()
      backbone = vision.resnet18(
          pretrained=pretrained, include_top=include_top, freeze=freeze
      )
      output_size = backbone.get_output_size()
      head = nn.Linear(output_size, num_classes)
      self.model = nn.Sequential(backbone, head)
  
file_path: BigDL/python/nano/test/pytorch/tests/train/trainer/test_trainer_precision.py
imports: import os
  from unittest import TestCase
  import pytest
  import torch
  from torch import nn
  from torch.utils.data import DataLoader, TensorDataset
  from pytorch_lightning.plugins.precision.native_amp import NativeMixedPrecisionPlugin
  from pytorch_lightning.plugins.precision.double import DoublePrecisionPlugin
  from bigdl.nano.pytorch import Trainer
  from bigdl.nano.pytorch.vision.models import vision
  from bigdl.nano.utils.pytorch import TORCH_VERSION_LESS_1_10
  from test.pytorch.utils._train_torch_lightning import create_data_loader, create_test_data_loader, data_transform
label_desc: Use image node_id 1 to create a new ResNet18 object from inherited base classes: nn with example: obj = ResNet18(num_classes, pretrained, include_top, freeze)